alakxender/e5-dhivehi-paws-cos

SentenceTransformer

This is a sentence-transformers model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• —Model Type: Sentence Transformer <!-- - Base model: Unknown -->
• —Maximum Sequence Length: 256 tokens
• —Output Dimensionality: 768 dimensions
• —Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

Fine-tuned Models

This model is part of a progressive series of sentence embedding models based on `intfloat/multilingual-e5-base`, fine-tuned specifically for Dhivehi language understanding.

Each stage leverages a targeted dataset to specialize the model for semantic similarity, question answering, and summarization tasks — improving performance for real-world Dhivehi NLP applications.

Each model builds upon the previous checkpoint, incrementally enhancing the semantic capabilities of the model for Dhivehi. The goal is to support high-quality sentence embeddings for a wide range of Dhivehi information retrieval and understanding tasks.

Model Sources

• —Documentation: Sentence Transformers Documentation
• —Repository: Sentence Transformers on GitHub
• —Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': 'XLMRobertaModel'})
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("alakxender/e5-dhivehi-cos")
# Run inference
sentences = [
    'query: އެފްރިކަން އެމެރިކާގެ ރޯޑިއޯ ޕާފޯމަރެއް ކަމަށްވާ ބިލް ޕިކެޓް ވެސް ވަނީ ހަމަ އެ ބެލުންތެރިންނަށް ހުޅަނގުގެ ކުރީގެ ފިލްމުތަކުން ފެނިގެން ގޮސްފައެވެ .',
    'query: އެފްރިކަން އެމެރިކާގެ ރޯޑިއޯއެއް ކަމަށްވާ ބިލް ބިލް ޕިކެޓް ވެސް ހުޅަނގުގެ ކުރީގެ ފިލްމުތަކުން ފެނިގެން ގޮސްފައިވަނީ ހަމަ އެ ބެލުންތެރިންނަށް ކަމަށް ވެއެވެ .',
    'query: ވޯކްސް ޕްރޮގްރެސް އެޑްމިނިސްޓްރޭޝަންގެ ފެޑެރަލް ތިއޭޓަރ ޕްރޮޖެކްޓުން ވަޒީފާއެއް ނެތް ތިއޭޓަރ ޕާފޯމަރުންނާއި މުވައްޒަފުން މަސައްކަތް ކުރަން ކަނޑައެޅީ 1936 ވަނަ އަހަރު އެވެ .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.5215, 0.1699],
#         [0.5215, 1.0000, 0.0967],
#         [0.1699, 0.0967, 1.0000]])

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Downstream Usage (Sentence Transformers)

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Evaluation

Metrics

Semantic Similarity

• —Dataset: dhivehi-paws
• —Evaluated with <code>EmbeddingSimilarityEvaluator</code>

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Training Details

Training Dataset

• —Size: 57,401 training samples
• —Columns: <code>sentence0</code>, <code>sentence1</code>, and <code>label</code>
• —Approximate statistics based on the first 1000 samples: | | sentence0 | sentence1 | label | |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 17 tokens</li><li>mean: 42.83 tokens</li><li>max: 104 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 42.77 tokens</li><li>max: 89 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
• —Samples: | sentence0 | sentence1 | label | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| | <code>query: އެ ކުންފުނީގެ ހޯލްޑިން ކުންފުނި ގިނިސް ވޯލްޑް ރެކޯޑްސް ލިމިޓެޑަކީ 2001 ވަނަ އަހަރާ ހަމައަށް ގިނިސް ޕީއެލްސީ އާއި އޭގެ ފަހުން ޑައިޖިއޯގެ ކުންފުންޏެކެވެ .</code> | <code>query: އެ ކުންފުނީގެ ހޯލްޑިން ކުންފުނި ގިނިސް ވޯލްޑް ރެކޯޑްސް ލިމިޓެޑަކީ 2001 ވަނަ އަހަރާ ހަމައަށް ޑައިޖިއޯގެ ކުންފުންޏެއް ކަމަށްވާ ފަހުން ގިނިސް ޕީއެލްސީ އެވެ .</code> | <code>0.0</code> | | <code>query: ރަޝިޔާގެ ސެއިންޓް ޕީޓަރސްބަރގްގެ އަސްކަރީ ސްކޫލުން ޑިގްރީ ހާސިލް ކުރެއްވި އިރު 1910 ވަނަ އަހަރު ނިކޮލައެވްސްކްގެ މިލިޓަރީ އެކަޑަމީ ސޯފިއާ ޖެނެރަލް ސްޓާފް އިން ޑިގްރީ ހާސިލް ކުރެއްވިއެވެ .</code> | <code>query: ސޯފިއާގެ މިލިޓަރީ ސްކޫލުން ޑިގްރީ ހާސިލް ކުރެއްވި އިރު , 1910 ވަނަ އަހަރު ރަޝިޔާގެ ސެއިންޓް ޕީޓަރސްބާގްގައި ހުންނަ ނިކޮލައެވްސްކް ޖެނެރަލް ސްޓާފް މިލިޓަރީ އެކަޑަމީން ޑިގްރީ ހާސިލް ކުރެއްވިއެވެ .</code> | <code>0.0</code> | | <code>query: އަސްޓަރަވާސް އިން އުފައްދާ އެންމެ ރަނގަޅު ވައްތަރުގެ ސިލްކް އެކްސްޕޯޓް ކުރަނީ ދިމިޝްގު އާއި ބުރްސާ އާއި ކާޝަން އަދި ވެނިސް އަށެވެ .</code> | <code>query: އަސްޓަރަވާސް އިން އުފައްދާ އެންމެ ރަނގަޅު ސިލްކް އެކްސްޕޯޓް ކުރަނީ ވެނިސް އާއި ބުރްސާ އާއި ކާޝަން އަދި ދިމިޝްގު އަށެވެ .</code> | <code>1.0</code> |
• —Loss: <code>CosineSimilarityLoss</code> with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }

Training Hyperparameters

Non-Default Hyperparameters

• —eval_strategy: steps
• —per_device_train_batch_size: 32
• —per_device_eval_batch_size: 32
• —multi_dataset_batch_sampler: round_robin

All Hyperparameters

<details><summary>Click to expand</summary>

• —overwrite_output_dir: False
• —do_predict: False
• —eval_strategy: steps
• —prediction_loss_only: True
• —per_device_train_batch_size: 32
• —per_device_eval_batch_size: 32
• —per_gpu_train_batch_size: None
• —per_gpu_eval_batch_size: None
• —gradient_accumulation_steps: 1
• —eval_accumulation_steps: None
• —torch_empty_cache_steps: None
• —learning_rate: 5e-05
• —weight_decay: 0.0
• —adam_beta1: 0.9
• —adam_beta2: 0.999
• —adam_epsilon: 1e-08
• —max_grad_norm: 1
• —num_train_epochs: 3
• —max_steps: -1
• —lr_scheduler_type: linear
• —lr_scheduler_kwargs: {}
• —warmup_ratio: 0.0
• —warmup_steps: 0
• —log_level: passive
• —log_level_replica: warning
• —log_on_each_node: True
• —logging_nan_inf_filter: True
• —save_safetensors: True
• —save_on_each_node: False
• —save_only_model: False
• —restore_callback_states_from_checkpoint: False
• —no_cuda: False
• —use_cpu: False
• —use_mps_device: False
• —seed: 42
• —data_seed: None
• —jit_mode_eval: False
• —use_ipex: False
• —bf16: False
• —fp16: False
• —fp16_opt_level: O1
• —half_precision_backend: auto
• —bf16_full_eval: False
• —fp16_full_eval: False
• —tf32: None
• —local_rank: 0
• —ddp_backend: None
• —tpu_num_cores: None
• —tpu_metrics_debug: False
• —debug: []
• —dataloader_drop_last: False
• —dataloader_num_workers: 0
• —dataloader_prefetch_factor: None
• —past_index: -1
• —disable_tqdm: False
• —remove_unused_columns: True
• —label_names: None
• —load_best_model_at_end: False
• —ignore_data_skip: False
• —fsdp: []
• —fsdp_min_num_params: 0
• —fsdp_config: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —fsdp_transformer_layer_cls_to_wrap: None
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —deepspeed: None
• —label_smoothing_factor: 0.0
• —optim: adamw_torch
• —optim_args: None
• —adafactor: False
• —group_by_length: False
• —length_column_name: length
• —ddp_find_unused_parameters: None
• —ddp_bucket_cap_mb: None
• —ddp_broadcast_buffers: False
• —dataloader_pin_memory: True
• —dataloader_persistent_workers: False
• —skip_memory_metrics: True
• —use_legacy_prediction_loop: False
• —push_to_hub: False
• —resume_from_checkpoint: None
• —hub_model_id: None
• —hub_strategy: every_save
• —hub_private_repo: None
• —hub_always_push: False
• —gradient_checkpointing: False
• —gradient_checkpointing_kwargs: None
• —include_inputs_for_metrics: False
• —include_for_metrics: []
• —eval_do_concat_batches: True
• —fp16_backend: auto
• —push_to_hub_model_id: None
• —push_to_hub_organization: None
• —mp_parameters:
• —auto_find_batch_size: False
• —full_determinism: False
• —torchdynamo: None
• —ray_scope: last
• —ddp_timeout: 1800
• —torch_compile: False
• —torch_compile_backend: None
• —torch_compile_mode: None
• —include_tokens_per_second: False
• —include_num_input_tokens_seen: False
• —neftune_noise_alpha: None
• —optim_target_modules: None
• —batch_eval_metrics: False
• —eval_on_start: False
• —use_liger_kernel: False
• —eval_use_gather_object: False
• —average_tokens_across_devices: False
• —prompts: None
• —batch_sampler: batch_sampler
• —multi_dataset_batch_sampler: round_robin
• —router_mapping: {}
• —learning_rate_mapping: {}

</details>

Training Logs

Framework Versions

• —Python: 3.9.21
• —Sentence Transformers: 5.0.0
• —Transformers: 4.52.4
• —PyTorch: 2.5.1+cu124
• —Accelerate: 1.3.0
• —Datasets: 3.2.0
• —Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

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